Overview

Dataset statistics

Number of variables23
Number of observations27936
Missing cells31205
Missing cells (%)4.9%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory4.9 MiB
Average record size in memory184.0 B

Variable types

DateTime3
Numeric13
Text4
Categorical3

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
language is highly imbalanced (58.2%)Imbalance
minWind has 9959 (35.6%) missing valuesMissing
maxWind has 9959 (35.6%) missing valuesMissing
wind has 11233 (40.2%) missing valuesMissing
minTemperature has 713 (2.6%) zerosZeros
precipitation has 26081 (93.4%) zerosZeros
minWind has 7112 (25.5%) zerosZeros
tot_curr_cell has 7644 (27.4%) zerosZeros
NR_SITES has 7511 (26.9%) zerosZeros
curr_site has 7644 (27.4%) zerosZeros

Reproduction

Analysis started2024-09-21 13:58:48.186296
Analysis finished2024-09-21 13:59:49.888716
Duration1 minute and 1.7 second
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

date
Date

Distinct61
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size218.4 KiB
Minimum2013-11-01 00:00:00
Maximum2013-12-31 00:00:00
2024-09-21T15:59:50.288312image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:51.110775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

time
Date

Distinct1416
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size218.4 KiB
Minimum2024-09-21 00:00:00
Maximum2024-09-21 23:59:00
2024-09-21T15:59:51.510109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:51.937459image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

timestamp_x
Real number (ℝ)

Distinct27911
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3864202 × 109
Minimum1.3832605 × 109
Maximum1.3885307 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size218.4 KiB
2024-09-21T15:59:52.475150image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.3832605 × 109
5-th percentile1.383785 × 109
Q11.3852807 × 109
median1.3865446 × 109
Q31.3877466 × 109
95-th percentile1.3884158 × 109
Maximum1.3885307 × 109
Range5270191
Interquartile range (IQR)2465898

Descriptive statistics

Standard deviation1477254.7
Coefficient of variation (CV)0.0010655173
Kurtosis-0.97034676
Mean1.3864202 × 109
Median Absolute Deviation (MAD)1248725.5
Skewness-0.35162516
Sum3.8731036 × 1013
Variance2.1822815 × 1012
MonotonicityNot monotonic
2024-09-21T15:59:52.964584image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1388353923 2
 
< 0.1%
1387109797 2
 
< 0.1%
1386360081 2
 
< 0.1%
1388267678 2
 
< 0.1%
1387741582 2
 
< 0.1%
1385837337 2
 
< 0.1%
1384818973 2
 
< 0.1%
1388516440 2
 
< 0.1%
1388506837 2
 
< 0.1%
1385573790 2
 
< 0.1%
Other values (27901) 27916
99.9%
ValueCountFrequency (%)
1383260474 1
< 0.1%
1383260656 1
< 0.1%
1383262717 1
< 0.1%
1383262983 1
< 0.1%
1383263027 1
< 0.1%
1383263117 1
< 0.1%
1383263140 1
< 0.1%
1383263624 1
< 0.1%
1383263968 1
< 0.1%
1383265510 1
< 0.1%
ValueCountFrequency (%)
1388530665 1
< 0.1%
1388530652 1
< 0.1%
1388530531 1
< 0.1%
1388530339 1
< 0.1%
1388530331 1
< 0.1%
1388530311 1
< 0.1%
1388530272 1
< 0.1%
1388530214 1
< 0.1%
1388530136 1
< 0.1%
1388530106 1
< 0.1%

user
Text

Distinct2599
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size218.4 KiB
2024-09-21T15:59:53.507470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters279360
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1104 ?
Unique (%)4.0%

Sample

1st row5fd4f31f75
2nd row68c0e98182
3rd rowabe21fc052
4th row94d1efbbfd
5th rowd261d03075
ValueCountFrequency (%)
513fde95e6 2655
 
9.5%
cd79302fa7 2442
 
8.7%
e61ce711d3 1724
 
6.2%
8a3a60668f 1039
 
3.7%
9e27de3230 803
 
2.9%
6a9b803f81 671
 
2.4%
8b6e47b4dd 506
 
1.8%
40c932449b 381
 
1.4%
757a7a14c1 345
 
1.2%
bfcd7fc2b7 345
 
1.2%
Other values (2589) 17025
60.9%
2024-09-21T15:59:54.327039image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 20785
 
7.4%
7 20784
 
7.4%
1 20739
 
7.4%
d 20466
 
7.3%
3 19998
 
7.2%
6 19680
 
7.0%
9 18190
 
6.5%
f 17279
 
6.2%
a 16700
 
6.0%
c 16522
 
5.9%
Other values (6) 88217
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 279360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 20785
 
7.4%
7 20784
 
7.4%
1 20739
 
7.4%
d 20466
 
7.3%
3 19998
 
7.2%
6 19680
 
7.0%
9 18190
 
6.5%
f 17279
 
6.2%
a 16700
 
6.0%
c 16522
 
5.9%
Other values (6) 88217
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 279360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 20785
 
7.4%
7 20784
 
7.4%
1 20739
 
7.4%
d 20466
 
7.3%
3 19998
 
7.2%
6 19680
 
7.0%
9 18190
 
6.5%
f 17279
 
6.2%
a 16700
 
6.0%
c 16522
 
5.9%
Other values (6) 88217
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 279360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 20785
 
7.4%
7 20784
 
7.4%
1 20739
 
7.4%
d 20466
 
7.3%
3 19998
 
7.2%
6 19680
 
7.0%
9 18190
 
6.5%
f 17279
 
6.2%
a 16700
 
6.0%
c 16522
 
5.9%
Other values (6) 88217
31.6%
Distinct159
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size218.4 KiB
2024-09-21T15:59:54.897419image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length25
Median length21
Mean length7.9234679
Min length3

Characters and Unicode

Total characters221350
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowTrento
2nd rowPinzolo
3rd rowCavalese
4th rowRovereto
5th rowSan Michele all'Adige
ValueCountFrequency (%)
trento 8015
23.8%
rovereto 3019
 
9.0%
tuenno 2817
 
8.4%
terme 1445
 
4.3%
valsugana 1184
 
3.5%
di 1171
 
3.5%
sant'orsola 1005
 
3.0%
tesero 892
 
2.7%
fassa 777
 
2.3%
pinzolo 735
 
2.2%
Other values (172) 12549
37.3%
2024-09-21T15:59:55.730491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 30766
13.9%
e 30027
13.6%
n 22627
10.2%
r 19045
 
8.6%
a 17151
 
7.7%
T 13513
 
6.1%
t 12361
 
5.6%
i 8196
 
3.7%
l 7219
 
3.3%
s 7098
 
3.2%
Other values (35) 53347
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 221350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 30766
13.9%
e 30027
13.6%
n 22627
10.2%
r 19045
 
8.6%
a 17151
 
7.7%
T 13513
 
6.1%
t 12361
 
5.6%
i 8196
 
3.7%
l 7219
 
3.3%
s 7098
 
3.2%
Other values (35) 53347
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 221350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 30766
13.9%
e 30027
13.6%
n 22627
10.2%
r 19045
 
8.6%
a 17151
 
7.7%
T 13513
 
6.1%
t 12361
 
5.6%
i 8196
 
3.7%
l 7219
 
3.3%
s 7098
 
3.2%
Other values (35) 53347
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 221350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 30766
13.9%
e 30027
13.6%
n 22627
10.2%
r 19045
 
8.6%
a 17151
 
7.7%
T 13513
 
6.1%
t 12361
 
5.6%
i 8196
 
3.7%
l 7219
 
3.3%
s 7098
 
3.2%
Other values (35) 53347
24.1%

language
Categorical

IMBALANCE 

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size218.4 KiB
it
17907 
en
4025 
es
 
1126
und
 
1027
pt
 
845
Other values (29)
3006 

Length

Max length3
Median length2
Mean length2.0367626
Min length2

Characters and Unicode

Total characters56899
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowit
2nd rowtl
3rd rowen
4th rowit
5th rowru

Common Values

ValueCountFrequency (%)
it 17907
64.1%
en 4025
 
14.4%
es 1126
 
4.0%
und 1027
 
3.7%
pt 845
 
3.0%
in 391
 
1.4%
tr 285
 
1.0%
tl 279
 
1.0%
sk 240
 
0.9%
de 230
 
0.8%
Other values (24) 1581
 
5.7%

Length

2024-09-21T15:59:56.108838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
it 17907
64.1%
en 4025
 
14.4%
es 1126
 
4.0%
und 1027
 
3.7%
pt 845
 
3.0%
in 391
 
1.4%
tr 285
 
1.0%
tl 279
 
1.0%
sk 240
 
0.9%
de 230
 
0.8%
Other values (24) 1581
 
5.7%

Most occurring characters

ValueCountFrequency (%)
t 19593
34.4%
i 18464
32.5%
n 5576
 
9.8%
e 5538
 
9.7%
s 1510
 
2.7%
d 1330
 
2.3%
u 1276
 
2.2%
p 898
 
1.6%
r 718
 
1.3%
l 713
 
1.3%
Other values (11) 1283
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56899
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 19593
34.4%
i 18464
32.5%
n 5576
 
9.8%
e 5538
 
9.7%
s 1510
 
2.7%
d 1330
 
2.3%
u 1276
 
2.2%
p 898
 
1.6%
r 718
 
1.3%
l 713
 
1.3%
Other values (11) 1283
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56899
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 19593
34.4%
i 18464
32.5%
n 5576
 
9.8%
e 5538
 
9.7%
s 1510
 
2.7%
d 1330
 
2.3%
u 1276
 
2.2%
p 898
 
1.6%
r 718
 
1.3%
l 713
 
1.3%
Other values (11) 1283
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56899
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 19593
34.4%
i 18464
32.5%
n 5576
 
9.8%
e 5538
 
9.7%
s 1510
 
2.7%
d 1330
 
2.3%
u 1276
 
2.2%
p 898
 
1.6%
r 718
 
1.3%
l 713
 
1.3%
Other values (11) 1283
 
2.3%
Distinct144
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size218.4 KiB
Minimum2024-09-21 00:00:00
Maximum2024-09-21 23:50:00
2024-09-21T15:59:56.474811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:56.885985image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct980
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size218.4 KiB
2024-09-21T15:59:57.476560image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length19
Median length19
Mean length18.793063
Min length15

Characters and Unicode

Total characters525003
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique270 ?
Unique (%)1.0%

Sample

1st rowPOINT (11.13 46.07)
2nd rowPOINT (10.83 46.23)
3rd rowPOINT (11.46 46.29)
4th rowPOINT (11.04 45.89)
5th rowPOINT (11.12 46.2)
ValueCountFrequency (%)
point 27936
33.3%
11.12 3419
 
4.1%
46.07 3009
 
3.6%
11.02 2995
 
3.6%
46.33 2873
 
3.4%
46.06 2295
 
2.7%
11.13 2268
 
2.7%
11.03 1693
 
2.0%
46.29 1558
 
1.9%
46.05 1447
 
1.7%
Other values (177) 34315
40.9%
2024-09-21T15:59:58.556727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 73207
13.9%
55872
10.6%
. 55598
10.6%
4 37128
 
7.1%
6 31433
 
6.0%
P 27936
 
5.3%
I 27936
 
5.3%
N 27936
 
5.3%
T 27936
 
5.3%
( 27936
 
5.3%
Other values (9) 132085
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 525003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 73207
13.9%
55872
10.6%
. 55598
10.6%
4 37128
 
7.1%
6 31433
 
6.0%
P 27936
 
5.3%
I 27936
 
5.3%
N 27936
 
5.3%
T 27936
 
5.3%
( 27936
 
5.3%
Other values (9) 132085
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 525003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 73207
13.9%
55872
10.6%
. 55598
10.6%
4 37128
 
7.1%
6 31433
 
6.0%
P 27936
 
5.3%
I 27936
 
5.3%
N 27936
 
5.3%
T 27936
 
5.3%
( 27936
 
5.3%
Other values (9) 132085
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 525003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 73207
13.9%
55872
10.6%
. 55598
10.6%
4 37128
 
7.1%
6 31433
 
6.0%
P 27936
 
5.3%
I 27936
 
5.3%
N 27936
 
5.3%
T 27936
 
5.3%
( 27936
 
5.3%
Other values (9) 132085
25.2%

station
Categorical

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size218.4 KiB
T0129
6679 
T0397
3508 
T0147
3238 
T0367
1754 
T0139
1414 
Other values (26)
11343 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters139680
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowT0129
2nd rowT0071
3rd rowT0367
4th rowT0147
5th rowT0408

Common Values

ValueCountFrequency (%)
T0129 6679
23.9%
T0397 3508
12.6%
T0147 3238
11.6%
T0367 1754
 
6.3%
T0139 1414
 
5.1%
T0096 1255
 
4.5%
T0135 1139
 
4.1%
T0409 1057
 
3.8%
T0071 949
 
3.4%
T0437 852
 
3.0%
Other values (21) 6091
21.8%

Length

2024-09-21T15:59:58.946200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
t0129 6679
23.9%
t0397 3508
12.6%
t0147 3238
11.6%
t0367 1754
 
6.3%
t0139 1414
 
5.1%
t0096 1255
 
4.5%
t0135 1139
 
4.1%
t0409 1057
 
3.8%
t0071 949
 
3.4%
t0437 852
 
3.0%
Other values (21) 6091
21.8%

Most occurring characters

ValueCountFrequency (%)
0 32907
23.6%
T 27936
20.0%
9 16329
11.7%
1 16272
11.6%
3 11235
 
8.0%
7 10914
 
7.8%
2 8398
 
6.0%
4 7417
 
5.3%
6 4677
 
3.3%
5 1883
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32907
23.6%
T 27936
20.0%
9 16329
11.7%
1 16272
11.6%
3 11235
 
8.0%
7 10914
 
7.8%
2 8398
 
6.0%
4 7417
 
5.3%
6 4677
 
3.3%
5 1883
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32907
23.6%
T 27936
20.0%
9 16329
11.7%
1 16272
11.6%
3 11235
 
8.0%
7 10914
 
7.8%
2 8398
 
6.0%
4 7417
 
5.3%
6 4677
 
3.3%
5 1883
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32907
23.6%
T 27936
20.0%
9 16329
11.7%
1 16272
11.6%
3 11235
 
8.0%
7 10914
 
7.8%
2 8398
 
6.0%
4 7417
 
5.3%
6 4677
 
3.3%
5 1883
 
1.3%

elevation
Real number (ℝ)

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean589.57861
Minimum85
Maximum1470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size218.4 KiB
2024-09-21T15:59:59.277244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum85
5-th percentile194
Q1312
median458
Q3925
95-th percentile1205
Maximum1470
Range1385
Interquartile range (IQR)613

Descriptive statistics

Standard deviation363.14073
Coefficient of variation (CV)0.61593267
Kurtosis-0.57305551
Mean589.57861
Median Absolute Deviation (MAD)255
Skewness0.68048339
Sum16470468
Variance131871.19
MonotonicityNot monotonic
2024-09-21T15:59:59.666238image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
312 6679
23.9%
655 3508
12.6%
203 3238
11.6%
958 2372
 
8.5%
925 1414
 
5.1%
1205 1255
 
4.5%
194 1139
 
4.1%
458 1057
 
3.8%
905 949
 
3.4%
1465 852
 
3.0%
Other values (20) 5473
19.6%
ValueCountFrequency (%)
85 6
 
< 0.1%
170 14
 
0.1%
182 329
 
1.2%
194 1139
 
4.1%
201 454
 
1.6%
203 3238
11.6%
204 478
 
1.7%
252 158
 
0.6%
282 86
 
0.3%
312 6679
23.9%
ValueCountFrequency (%)
1470 343
 
1.2%
1465 852
 
3.0%
1205 1255
4.5%
1155 108
 
0.4%
1121 167
 
0.6%
1055 7
 
< 0.1%
1045 114
 
0.4%
1000 511
 
1.8%
958 2372
8.5%
925 1414
5.1%

timestamp_y
Real number (ℝ)

Distinct61
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3863678 × 109
Minimum1.3832604 × 109
Maximum1.3884444 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size218.4 KiB
2024-09-21T16:00:00.125677image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.3832604 × 109
5-th percentile1.3837788 × 109
Q11.3852476 × 109
median1.3865436 × 109
Q31.3876668 × 109
95-th percentile1.388358 × 109
Maximum1.3884444 × 109
Range5184000
Interquartile range (IQR)2419200

Descriptive statistics

Standard deviation1476622.1
Coefficient of variation (CV)0.0010651013
Kurtosis-0.97145184
Mean1.3863678 × 109
Median Absolute Deviation (MAD)1209600
Skewness-0.35046627
Sum3.872957 × 1013
Variance2.1804128 × 1012
MonotonicityNot monotonic
2024-09-21T16:00:00.703756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1388271600 1044
 
3.7%
1388358000 968
 
3.5%
1388444400 961
 
3.4%
1388185200 885
 
3.2%
1388098800 744
 
2.7%
1388012400 720
 
2.6%
1387062000 715
 
2.6%
1386457200 690
 
2.5%
1387839600 640
 
2.3%
1386889200 626
 
2.2%
Other values (51) 19943
71.4%
ValueCountFrequency (%)
1383260400 276
1.0%
1383346800 209
0.7%
1383433200 203
0.7%
1383519600 180
0.6%
1383606000 285
1.0%
1383692400 206
0.7%
1383778800 229
0.8%
1383865200 312
1.1%
1383951600 408
1.5%
1384038000 363
1.3%
ValueCountFrequency (%)
1388444400 961
3.4%
1388358000 968
3.5%
1388271600 1044
3.7%
1388185200 885
3.2%
1388098800 744
2.7%
1388012400 720
2.6%
1387926000 602
2.2%
1387839600 640
2.3%
1387753200 375
 
1.3%
1387666800 534
1.9%

minTemperature
Real number (ℝ)

ZEROS 

Distinct200
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35509021
Minimum-12.4
Maximum12.7
Zeros713
Zeros (%)2.6%
Negative12939
Negative (%)46.3%
Memory size218.4 KiB
2024-09-21T16:00:01.095845image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-12.4
5-th percentile-5.7
Q1-2.4
median0.1
Q32.8
95-th percentile7.7
Maximum12.7
Range25.1
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation4.1450395
Coefficient of variation (CV)11.673201
Kurtosis0.14127055
Mean0.35509021
Median Absolute Deviation (MAD)2.6
Skewness0.27398066
Sum9919.8
Variance17.181352
MonotonicityNot monotonic
2024-09-21T16:00:01.474870image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 713
 
2.6%
-1 607
 
2.2%
0.1 567
 
2.0%
0.9 536
 
1.9%
-1.6 491
 
1.8%
2.4 470
 
1.7%
3.2 452
 
1.6%
0.8 424
 
1.5%
1 421
 
1.5%
0.6 420
 
1.5%
Other values (190) 22835
81.7%
ValueCountFrequency (%)
-12.4 9
 
< 0.1%
-11.9 2
 
< 0.1%
-11.8 14
 
0.1%
-11.4 2
 
< 0.1%
-10.5 2
 
< 0.1%
-10.2 52
 
0.2%
-10 149
0.5%
-9.9 84
0.3%
-9.6 10
 
< 0.1%
-9.4 10
 
< 0.1%
ValueCountFrequency (%)
12.7 4
 
< 0.1%
12.2 52
 
0.2%
11.7 106
0.4%
11.5 32
 
0.1%
11.4 154
0.6%
11.1 16
 
0.1%
11 16
 
0.1%
10.9 13
 
< 0.1%
10.6 53
 
0.2%
10.5 19
 
0.1%

maxTemperature
Real number (ℝ)

Distinct194
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5115156
Minimum-5
Maximum19.3
Zeros2
Zeros (%)< 0.1%
Negative579
Negative (%)2.1%
Memory size218.4 KiB
2024-09-21T16:00:01.852003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile1.2
Q14.9
median7.5
Q39.9
95-th percentile14.4
Maximum19.3
Range24.3
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8644016
Coefficient of variation (CV)0.51446363
Kurtosis0.17027637
Mean7.5115156
Median Absolute Deviation (MAD)2.5
Skewness0.041969422
Sum209841.7
Variance14.9336
MonotonicityNot monotonic
2024-09-21T16:00:02.264090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 603
 
2.2%
8.4 584
 
2.1%
7.4 544
 
1.9%
9 493
 
1.8%
9.8 467
 
1.7%
9.7 450
 
1.6%
6.9 449
 
1.6%
9.4 428
 
1.5%
5 428
 
1.5%
4.9 425
 
1.5%
Other values (184) 23065
82.6%
ValueCountFrequency (%)
-5 103
0.4%
-3.7 14
 
0.1%
-3 1
 
< 0.1%
-2.4 84
0.3%
-1.8 85
0.3%
-1.5 5
 
< 0.1%
-1.4 74
0.3%
-1.3 2
 
< 0.1%
-1.2 2
 
< 0.1%
-1.1 6
 
< 0.1%
ValueCountFrequency (%)
19.3 8
 
< 0.1%
18.9 33
0.1%
18.8 3
 
< 0.1%
18.5 6
 
< 0.1%
18.4 13
 
< 0.1%
18.1 62
0.2%
18 5
 
< 0.1%
17.7 11
 
< 0.1%
17.6 65
0.2%
17.5 16
 
0.1%

precipitation
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)0.1%
Missing27
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.035830736
Minimum0
Maximum5.4
Zeros26081
Zeros (%)93.4%
Negative0
Negative (%)0.0%
Memory size218.4 KiB
2024-09-21T16:00:02.624363image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.2
Maximum5.4
Range5.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1821874
Coefficient of variation (CV)5.0846681
Kurtosis85.899114
Mean0.035830736
Median Absolute Deviation (MAD)0
Skewness7.742462
Sum1000
Variance0.033192248
MonotonicityNot monotonic
2024-09-21T16:00:03.026798image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 26081
93.4%
0.2 826
 
3.0%
0.4 302
 
1.1%
0.8 202
 
0.7%
0.6 167
 
0.6%
1 124
 
0.4%
1.2 67
 
0.2%
1.4 52
 
0.2%
1.6 26
 
0.1%
2.2 19
 
0.1%
Other values (7) 43
 
0.2%
(Missing) 27
 
0.1%
ValueCountFrequency (%)
0 26081
93.4%
0.2 826
 
3.0%
0.4 302
 
1.1%
0.6 167
 
0.6%
0.8 202
 
0.7%
1 124
 
0.4%
1.2 67
 
0.2%
1.4 52
 
0.2%
1.6 26
 
0.1%
1.8 15
 
0.1%
ValueCountFrequency (%)
5.4 1
 
< 0.1%
3.4 1
 
< 0.1%
2.8 1
 
< 0.1%
2.6 1
 
< 0.1%
2.4 6
 
< 0.1%
2.2 19
 
0.1%
2 18
 
0.1%
1.8 15
 
0.1%
1.6 26
0.1%
1.4 52
0.2%

minWind
Real number (ℝ)

MISSING  ZEROS 

Distinct23
Distinct (%)0.1%
Missing9959
Missing (%)35.6%
Infinite0
Infinite (%)0.0%
Mean0.20118485
Minimum0
Maximum3.2
Zeros7112
Zeros (%)25.5%
Negative0
Negative (%)0.0%
Memory size218.4 KiB
2024-09-21T16:00:03.376513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1
Q30.3
95-th percentile0.7
Maximum3.2
Range3.2
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.30579305
Coefficient of variation (CV)1.5199606
Kurtosis18.172475
Mean0.20118485
Median Absolute Deviation (MAD)0.1
Skewness3.4155132
Sum3616.7
Variance0.093509388
MonotonicityNot monotonic
2024-09-21T16:00:03.702106image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 7112
25.5%
0.1 3183
 
11.4%
0.2 2702
 
9.7%
0.3 1872
 
6.7%
0.5 862
 
3.1%
0.4 720
 
2.6%
0.6 494
 
1.8%
0.8 243
 
0.9%
0.7 170
 
0.6%
1.2 166
 
0.6%
Other values (13) 453
 
1.6%
(Missing) 9959
35.6%
ValueCountFrequency (%)
0 7112
25.5%
0.1 3183
11.4%
0.2 2702
 
9.7%
0.3 1872
 
6.7%
0.4 720
 
2.6%
0.5 862
 
3.1%
0.6 494
 
1.8%
0.7 170
 
0.6%
0.8 243
 
0.9%
0.9 36
 
0.1%
ValueCountFrequency (%)
3.2 9
 
< 0.1%
3.1 5
 
< 0.1%
3 7
 
< 0.1%
2.7 1
 
< 0.1%
2.6 12
 
< 0.1%
1.8 9
 
< 0.1%
1.7 153
0.5%
1.6 56
 
0.2%
1.4 4
 
< 0.1%
1.3 7
 
< 0.1%

maxWind
Real number (ℝ)

MISSING 

Distinct88
Distinct (%)0.5%
Missing9959
Missing (%)35.6%
Infinite0
Infinite (%)0.0%
Mean3.0127719
Minimum0
Maximum13.6
Zeros47
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size218.4 KiB
2024-09-21T16:00:04.081971image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.9
median2.7
Q33.6
95-th percentile6
Maximum13.6
Range13.6
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.7751506
Coefficient of variation (CV)0.58920844
Kurtosis6.1910415
Mean3.0127719
Median Absolute Deviation (MAD)0.8
Skewness2.0000647
Sum54160.6
Variance3.1511597
MonotonicityNot monotonic
2024-09-21T16:00:04.507438image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 1667
 
6.0%
2.5 961
 
3.4%
2.1 721
 
2.6%
2.6 696
 
2.5%
2.7 690
 
2.5%
2.4 664
 
2.4%
1.6 549
 
2.0%
3.9 510
 
1.8%
1.3 504
 
1.8%
1.7 493
 
1.8%
Other values (78) 10522
37.7%
(Missing) 9959
35.6%
ValueCountFrequency (%)
0 47
 
0.2%
0.1 4
 
< 0.1%
0.2 83
 
0.3%
0.3 57
 
0.2%
0.4 49
 
0.2%
0.5 89
0.3%
0.6 83
 
0.3%
0.7 112
0.4%
0.8 48
 
0.2%
0.9 213
0.8%
ValueCountFrequency (%)
13.6 55
 
0.2%
11.8 9
 
< 0.1%
11.1 5
 
< 0.1%
10 6
 
< 0.1%
9.9 100
0.4%
9.4 19
 
0.1%
9.2 10
 
< 0.1%
9 97
0.3%
8.7 109
0.4%
8.4 183
0.7%

geometry_y
Categorical

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size218.4 KiB
POINT (11.13565308 46.07185136)
6679 
POINT (11.0399028 46.36116748)
3508 
POINT (11.04378514 45.89643672)
3238 
POINT (11.45171306 46.28478205)
1754 
POINT (11.30223033 46.10709297)
1414 
Other values (26)
11343 

Length

Max length31
Median length31
Mean length30.789841
Min length30

Characters and Unicode

Total characters860145
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOINT (11.13565308 46.07185136)
2nd rowPOINT (10.79582897 46.31340453)
3rd rowPOINT (11.45171306 46.28478205)
4th rowPOINT (11.04378514 45.89643672)
5th rowPOINT (11.10461227 46.18722699)

Common Values

ValueCountFrequency (%)
POINT (11.13565308 46.07185136) 6679
23.9%
POINT (11.0399028 46.36116748) 3508
12.6%
POINT (11.04378514 45.89643672) 3238
11.6%
POINT (11.45171306 46.28478205) 1754
 
6.3%
POINT (11.30223033 46.10709297) 1414
 
5.1%
POINT (11.6645808 46.38363633) 1255
 
4.5%
POINT (11.10130522 46.09564639) 1139
 
4.1%
POINT (11.24035332 46.05251107) 1057
 
3.8%
POINT (10.79582897 46.31340453) 949
 
3.4%
POINT (11.76685208 46.47831772) 852
 
3.0%
Other values (21) 6091
21.8%

Length

2024-09-21T16:00:04.874953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
point 27936
33.3%
46.07185136 6679
 
8.0%
11.13565308 6679
 
8.0%
11.0399028 3508
 
4.2%
46.36116748 3508
 
4.2%
11.04378514 3238
 
3.9%
45.89643672 3238
 
3.9%
11.45171306 1754
 
2.1%
46.28478205 1754
 
2.1%
11.30223033 1414
 
1.7%
Other values (53) 24100
28.8%

Most occurring characters

ValueCountFrequency (%)
1 106173
12.3%
6 71479
 
8.3%
3 63284
 
7.4%
4 57322
 
6.7%
55872
 
6.5%
. 55872
 
6.5%
0 53050
 
6.2%
5 49494
 
5.8%
8 45849
 
5.3%
7 44990
 
5.2%
Other values (9) 256760
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 860145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 106173
12.3%
6 71479
 
8.3%
3 63284
 
7.4%
4 57322
 
6.7%
55872
 
6.5%
. 55872
 
6.5%
0 53050
 
6.2%
5 49494
 
5.8%
8 45849
 
5.3%
7 44990
 
5.2%
Other values (9) 256760
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 860145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 106173
12.3%
6 71479
 
8.3%
3 63284
 
7.4%
4 57322
 
6.7%
55872
 
6.5%
. 55872
 
6.5%
0 53050
 
6.2%
5 49494
 
5.8%
8 45849
 
5.3%
7 44990
 
5.2%
Other values (9) 256760
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 860145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 106173
12.3%
6 71479
 
8.3%
3 63284
 
7.4%
4 57322
 
6.7%
55872
 
6.5%
. 55872
 
6.5%
0 53050
 
6.2%
5 49494
 
5.8%
8 45849
 
5.3%
7 44990
 
5.2%
Other values (9) 256760
29.9%

temperature
Real number (ℝ)

Distinct283
Distinct (%)1.0%
Missing27
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.6009853
Minimum-11.9
Maximum19.3
Zeros198
Zeros (%)0.7%
Negative5347
Negative (%)19.1%
Memory size218.4 KiB
2024-09-21T16:00:05.203557image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-11.9
5-th percentile-3.1
Q10.5
median3.5
Q36.6
95-th percentile10.8
Maximum19.3
Range31.2
Interquartile range (IQR)6.1

Descriptive statistics

Standard deviation4.319381
Coefficient of variation (CV)1.1994997
Kurtosis-0.077553691
Mean3.6009853
Median Absolute Deviation (MAD)3.1
Skewness0.084567964
Sum100499.9
Variance18.657052
MonotonicityNot monotonic
2024-09-21T16:00:05.604432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 377
 
1.3%
3.7 335
 
1.2%
0.3 333
 
1.2%
0.8 316
 
1.1%
1.3 309
 
1.1%
0.4 304
 
1.1%
0.1 301
 
1.1%
5.2 297
 
1.1%
1.1 289
 
1.0%
2.7 283
 
1.0%
Other values (273) 24765
88.6%
ValueCountFrequency (%)
-11.9 1
 
< 0.1%
-11.6 1
 
< 0.1%
-11.5 1
 
< 0.1%
-11.1 1
 
< 0.1%
-11 3
< 0.1%
-10.9 5
< 0.1%
-10.7 1
 
< 0.1%
-10.2 1
 
< 0.1%
-10.1 6
< 0.1%
-10 3
< 0.1%
ValueCountFrequency (%)
19.3 1
 
< 0.1%
18.8 1
 
< 0.1%
18.1 1
 
< 0.1%
17.9 1
 
< 0.1%
17.7 1
 
< 0.1%
17.6 3
< 0.1%
17.4 1
 
< 0.1%
17.2 1
 
< 0.1%
17.1 5
< 0.1%
17 2
 
< 0.1%

wind
Text

MISSING 

Distinct5279
Distinct (%)31.6%
Missing11233
Missing (%)40.2%
Memory size218.4 KiB
2024-09-21T16:00:06.196862image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.3688559
Min length3

Characters and Unicode

Total characters106379
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2075 ?
Unique (%)12.4%

Sample

1st row1@47
2nd row0.1@205
3rd row0.6@7
4th row0.7@72
5th row1.1@13
ValueCountFrequency (%)
1.7@291 86
 
0.5%
0.1@102 63
 
0.4%
0.3@86 60
 
0.4%
0.6@298 51
 
0.3%
1.2@71 51
 
0.3%
2.4@87 49
 
0.3%
1@206 42
 
0.3%
1.5@117 42
 
0.3%
0.9@213 41
 
0.2%
0@303 40
 
0.2%
Other values (5269) 16178
96.9%
2024-09-21T16:00:07.125175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
@ 16703
15.7%
1 14806
13.9%
. 14502
13.6%
0 10982
10.3%
2 10601
10.0%
3 10456
9.8%
4 5243
 
4.9%
5 5096
 
4.8%
7 4768
 
4.5%
6 4565
 
4.3%
Other values (2) 8657
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
@ 16703
15.7%
1 14806
13.9%
. 14502
13.6%
0 10982
10.3%
2 10601
10.0%
3 10456
9.8%
4 5243
 
4.9%
5 5096
 
4.8%
7 4768
 
4.5%
6 4565
 
4.3%
Other values (2) 8657
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
@ 16703
15.7%
1 14806
13.9%
. 14502
13.6%
0 10982
10.3%
2 10601
10.0%
3 10456
9.8%
4 5243
 
4.9%
5 5096
 
4.8%
7 4768
 
4.5%
6 4565
 
4.3%
Other values (2) 8657
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
@ 16703
15.7%
1 14806
13.9%
. 14502
13.6%
0 10982
10.3%
2 10601
10.0%
3 10456
9.8%
4 5243
 
4.9%
5 5096
 
4.8%
7 4768
 
4.5%
6 4565
 
4.3%
Other values (2) 8657
8.1%

cellId
Real number (ℝ)

Distinct870
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6275.3206
Minimum984
Maximum10985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size218.4 KiB
2024-09-21T16:00:07.819421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum984
5-th percentile2855
Q15083
median5550
Q38159
95-th percentile9998
Maximum10985
Range10001
Interquartile range (IQR)3076

Descriptive statistics

Standard deviation2158.8398
Coefficient of variation (CV)0.34402064
Kurtosis-0.88586668
Mean6275.3206
Median Absolute Deviation (MAD)1499
Skewness0.24691736
Sum1.7530736 × 108
Variance4660589.2
MonotonicityNot monotonic
2024-09-21T16:00:08.250955image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8585 2802
 
10.0%
5200 1473
 
5.3%
5083 1136
 
4.1%
5799 871
 
3.1%
2971 844
 
3.0%
5084 806
 
2.9%
8153 748
 
2.7%
2854 633
 
2.3%
5203 591
 
2.1%
4655 577
 
2.1%
Other values (860) 17455
62.5%
ValueCountFrequency (%)
984 3
< 0.1%
1227 2
< 0.1%
1341 1
 
< 0.1%
1343 2
< 0.1%
1457 3
< 0.1%
1573 3
< 0.1%
2044 3
< 0.1%
2267 1
 
< 0.1%
2268 3
< 0.1%
2269 1
 
< 0.1%
ValueCountFrequency (%)
10985 5
 
< 0.1%
10983 1
 
< 0.1%
10980 13
 
< 0.1%
10972 1
 
< 0.1%
10868 7
 
< 0.1%
10867 5
 
< 0.1%
10866 9
 
< 0.1%
10861 1
 
< 0.1%
10751 5
 
< 0.1%
10750 41
0.1%

tot_curr_cell
Real number (ℝ)

ZEROS 

Distinct14096
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.810105
Minimum-27.465555
Maximum260.84871
Zeros7644
Zeros (%)27.4%
Negative340
Negative (%)1.2%
Memory size218.4 KiB
2024-09-21T16:00:08.629875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-27.465555
5-th percentile0
Q10
median8.1732522
Q336.502628
95-th percentile102.02043
Maximum260.84871
Range288.31426
Interquartile range (IQR)36.502628

Descriptive statistics

Standard deviation38.360308
Coefficient of variation (CV)1.4862515
Kurtosis5.4624426
Mean25.810105
Median Absolute Deviation (MAD)8.1732522
Skewness2.1709879
Sum721031.1
Variance1471.5132
MonotonicityNot monotonic
2024-09-21T16:00:09.032926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7644
 
27.4%
17.63027655 49
 
0.2%
0.6310487268 48
 
0.2%
0.524701012 39
 
0.1%
155.283342 39
 
0.1%
42.84964523 36
 
0.1%
0.706899724 35
 
0.1%
8.426242531 33
 
0.1%
0.5684912291 31
 
0.1%
0.6396504453 30
 
0.1%
Other values (14086) 19952
71.4%
ValueCountFrequency (%)
-27.46555523 1
< 0.1%
-25.49027461 1
< 0.1%
-14.97397379 1
< 0.1%
-13.61678943 1
< 0.1%
-12.16422492 1
< 0.1%
-12.02622862 1
< 0.1%
-11.21786695 1
< 0.1%
-9.196839843 1
< 0.1%
-8.859649123 2
< 0.1%
-8.509334483 1
< 0.1%
ValueCountFrequency (%)
260.848707 1
< 0.1%
258.4916881 1
< 0.1%
255.920695 1
< 0.1%
254.7425565 1
< 0.1%
253.6655209 1
< 0.1%
249.8804823 1
< 0.1%
249.5278526 2
< 0.1%
248.4573409 1
< 0.1%
248.2185162 1
< 0.1%
246.4564832 2
< 0.1%

NR_SITES
Real number (ℝ)

ZEROS 

Distinct255
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean286.55462
Minimum0
Maximum1288
Zeros7511
Zeros (%)26.9%
Negative0
Negative (%)0.0%
Memory size218.4 KiB
2024-09-21T16:00:09.408037image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median180
Q3500
95-th percentile995
Maximum1288
Range1288
Interquartile range (IQR)500

Descriptive statistics

Standard deviation330.92221
Coefficient of variation (CV)1.1548312
Kurtosis0.87652765
Mean286.55462
Median Absolute Deviation (MAD)180
Skewness1.1928872
Sum8005190
Variance109509.51
MonotonicityNot monotonic
2024-09-21T16:00:09.809013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7511
26.9%
684 1473
 
5.3%
500 1136
 
4.1%
213 845
 
3.0%
1198 806
 
2.9%
34 774
 
2.8%
520 633
 
2.3%
411 619
 
2.2%
289 577
 
2.1%
995 576
 
2.1%
Other values (245) 12986
46.5%
ValueCountFrequency (%)
0 7511
26.9%
1 392
 
1.4%
2 280
 
1.0%
3 106
 
0.4%
4 109
 
0.4%
5 30
 
0.1%
6 66
 
0.2%
7 138
 
0.5%
8 22
 
0.1%
9 148
 
0.5%
ValueCountFrequency (%)
1288 441
 
1.6%
1198 806
2.9%
995 576
 
2.1%
766 120
 
0.4%
706 17
 
0.1%
693 184
 
0.7%
691 38
 
0.1%
685 66
 
0.2%
684 1473
5.3%
670 1
 
< 0.1%

curr_site
Real number (ℝ)

ZEROS 

Distinct13884
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.072070036
Minimum-0.14649583
Maximum5.7850001
Zeros7644
Zeros (%)27.4%
Negative340
Negative (%)1.2%
Memory size218.4 KiB
2024-09-21T16:00:10.199459image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-0.14649583
5-th percentile0
Q10
median0.044287681
Q30.092864404
95-th percentile0.18669489
Maximum5.7850001
Range5.9314959
Interquartile range (IQR)0.092864404

Descriptive statistics

Standard deviation0.16248512
Coefficient of variation (CV)2.2545447
Kurtosis240.35645
Mean0.072070036
Median Absolute Deviation (MAD)0.044287681
Skewness11.817625
Sum2013.3485
Variance0.026401414
MonotonicityNot monotonic
2024-09-21T16:00:10.586312image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7644
 
27.4%
0.06186061948 49
 
0.2%
0.01856025667 48
 
0.2%
0.1296188163 39
 
0.1%
0.0154323827 39
 
0.1%
0.07106077152 36
 
0.1%
0.02079116835 35
 
0.1%
0.02846703558 33
 
0.1%
0.01672033027 31
 
0.1%
0.01881324839 30
 
0.1%
Other values (13874) 19952
71.4%
ValueCountFrequency (%)
-0.1464958311 1
 
< 0.1%
-0.1098622209 1
 
< 0.1%
-0.08239244143 1
 
< 0.1%
-0.08031702113 1
 
< 0.1%
-0.06948080653 1
 
< 0.1%
-0.06262660365 3
< 0.1%
-0.05989589516 1
 
< 0.1%
-0.05985820527 1
 
< 0.1%
-0.05757933964 1
 
< 0.1%
-0.05673531533 1
 
< 0.1%
ValueCountFrequency (%)
5.7850001 1
< 0.1%
5.449166867 1
< 0.1%
4.980833433 1
< 0.1%
4.946666467 1
< 0.1%
4.007499933 1
< 0.1%
3.7100001 1
< 0.1%
3.338504157 1
< 0.1%
2.876666767 1
< 0.1%
2.683720977 1
< 0.1%
2.446283703 1
< 0.1%

Interactions

2024-09-21T15:59:43.558698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:58:56.494031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:00.168955image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:03.823767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:07.492222image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:11.504099image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:15.368848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:18.844927image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:22.666346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:27.217377image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:30.980566image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:35.101509image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:38.974517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:43.859786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:58:56.766950image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:00.441458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:04.107863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:07.776770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:11.784317image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:15.630504image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:19.109741image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:22.958349image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:27.519626image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:31.249980image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:35.407873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:39.277740image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:44.151283image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:58:57.046021image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:00.700970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:04.426301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:08.048005image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:12.069910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:15.902744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:19.393928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:23.292579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:27.813494image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:31.557683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:35.699608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:39.564599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:44.448912image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:58:57.307571image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:01.080946image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:04.692735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:08.375959image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:12.385028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:16.155640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:19.667179image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:23.579436image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:28.098210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:31.958379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:35.985281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:39.834027image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:44.723685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:58:57.585991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:01.353859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:04.983704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:08.727368image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:12.638772image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:16.411009image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:19.923565image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:23.866420image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:28.400089image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:32.228409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:36.247418image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:40.093849image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:44.999992image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:58:57.862631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:01.608191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:05.285747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:09.089194image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:12.886871image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:16.660470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:20.213599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:24.177174image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:28.665277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:32.799450image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:36.543873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:40.372718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:45.258353image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:58:58.162877image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:01.859200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:05.538295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:09.378916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:13.181810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:16.907907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:20.499023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:24.500646image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:28.941324image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:33.055958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:36.814894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:40.665013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:45.556282image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:58:58.439383image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:02.128981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:05.815668image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:09.667820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:13.478186image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:17.173363image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:20.808345image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:24.839767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:29.221802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:33.396631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:37.117860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:40.954364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:45.845140image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:58:58.744446image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:02.426283image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:06.168383image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:09.970014image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:13.746197image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:17.456972image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:21.124666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:25.163535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:29.526008image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:33.695350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:37.423857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:41.304553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:46.137430image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:58:59.040103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:02.700049image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:06.449188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:10.260294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:14.283635image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:17.750718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:21.563806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:25.520850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:29.823783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:33.984506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:37.759927image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:41.842636image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:46.396918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:58:59.303549image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:02.951363image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:06.691774image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:10.519251image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:14.537198image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:18.004182image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:21.828725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:25.900148image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:30.127049image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:34.266602image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:38.040929image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:42.366355image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:46.698272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:58:59.579202image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:03.251819image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:06.964995image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:10.792817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:14.830275image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:18.307122image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:22.095921image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:26.550903image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:30.408619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:34.547330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:38.350364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:42.791629image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:46.988460image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:58:59.879368image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:03.549998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:07.230271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:11.168345image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:15.096248image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:18.579889image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:22.366230image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:26.910720image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:30.706851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:34.826253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:38.668028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-09-21T15:59:43.218164image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-09-21T15:59:47.482971image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-21T15:59:48.561944image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datetimetimestamp_xusermunicipality.namelanguagehour_blocksgeometrystationelevationtimestamp_yminTemperaturemaxTemperatureprecipitationminWindmaxWindgeometry_ytemperaturewindcellIdtot_curr_cellNR_SITEScurr_site
02013-11-0100:0413832606565fd4f31f75Trentoit00:00POINT (11.13 46.07)T0129312138326040011.716.00.00.22.0POINT (11.13565308 46.07185136)13.21@47520190.9719401288.00.070630
12013-11-0100:01138326047468c0e98182Pinzolotl00:00POINT (10.83 46.23)T007190513832604004.512.30.00.02.5POINT (10.79582897 46.31340453)8.40.1@20571671.78458624.00.074358
22013-11-0100:431383262983abe21fc052Cavaleseen00:40POINT (11.46 46.29)T036795813832604007.013.00.00.01.8POINT (11.45171306 46.28478205)8.8NaN81510.0000000.00.000000
32013-11-0100:38138326271794d1efbbfdRoveretoit00:30POINT (11.04 45.89)T0147203138326040011.018.40.00.21.9POINT (11.04378514 45.89643672)13.20.6@7285547.591226995.00.047830
42013-11-0100:451383263140d261d03075San Michele all'Adigeru00:40POINT (11.12 46.2)T040820413832604009.616.90.0NaNNaNPOINT (11.10461227 46.18722699)11.6NaN68380.1003724.00.025093
52013-11-0100:45138326311746c436c9f1Trentoes00:40POINT (11.13 46.06)T0129312138326040011.716.00.00.22.0POINT (11.13565308 46.07185136)12.80.7@72508483.9602191198.00.070084
62013-11-0500:521383609129cd79302fa7Roveretound00:50POINT (11.03 45.89)T014720313836060007.716.40.00.12.8POINT (11.04378514 45.89643672)9.01.1@13285434.892401520.00.067101
72013-11-0101:2513832655105fd4f31f75Trentoit01:20POINT (11.13 46.07)T0129312138326040011.716.00.00.22.0POINT (11.13565308 46.07185136)12.20.3@68520181.9345921288.00.063614
82013-11-0101:3813832663205fd4f31f75Trentopt01:30POINT (11.13 46.07)T0129312138326040011.716.00.00.22.0POINT (11.13565308 46.07185136)12.41@48520180.4677501288.00.062475
92013-11-0101:2813832657174294c6de56Pinzoloen01:20POINT (10.82 46.23)T007190513832604004.512.30.00.02.5POINT (10.79582897 46.31340453)8.2NaN716620.597521272.00.075726
datetimetimestamp_xusermunicipality.namelanguagehour_blocksgeometrystationelevationtimestamp_yminTemperaturemaxTemperatureprecipitationminWindmaxWindgeometry_ytemperaturewindcellIdtot_curr_cellNR_SITEScurr_site
279262013-12-2823:0313882681806b25bf7143Caranoit23:00POINT (11.43 46.29)T03679581388185200-2.24.50.00.01.5POINT (11.45171306 46.28478205)0.80@96814925.144990466.00.053959
279272013-12-2919:45138834272099f8df359cPinzolound19:40POINT (10.82 46.23)T00719051388271600-1.62.50.00.01.3POINT (10.79582897 46.31340453)0.6NaN716664.261435272.00.236255
279282013-12-2214:2313877186196a9b803f81Livoit14:20POINT (11.01 46.4)T039765513876668003.26.50.0NaNNaNPOINT (11.0399028 46.36116748)6.0NaN94030.47891649.00.009774
279292013-12-1923:2113874917182f3ea1c3a4Trentoen23:20POINT (11.15 46.06)T01293121387407600-1.41.60.20.32.5POINT (11.13565308 46.07185136)0.21.2@308508615.923988396.00.040212
279302013-12-2916:441388331867fcc75ded96Trentoit16:40POINT (11.12 46.06)T012931213882716003.25.90.00.22.4POINT (11.13565308 46.07185136)5.90.8@50508328.773956500.00.057548
279312013-12-3100:251388445948826558e00eVigo di Fassait00:20POINT (11.68 46.42)T009612051388444400-10.00.30.0NaNNaNPOINT (11.6645808 46.38363633)-7.8NaN99220.0000000.00.000000
279322013-12-2913:12138831916606e9b1cdffPredazzoit13:10POINT (11.6 46.31)T0389100013882716000.43.70.0NaNNaNPOINT (11.59824667 46.29784352)3.4NaN85130.0000000.00.000000
279332013-11-1900:511384818699e61ce711d3Sant'Orsola Termeit00:50POINT (11.3 46.11)T013992513848156004.36.10.0NaNNaNPOINT (11.30223033 46.10709297)4.8NaN57990.0000000.00.000000
279342013-11-2309:491385196576e61ce711d3Sant'Orsola Termeit09:40POINT (11.3 46.11)T01399251385161200-1.62.60.0NaNNaNPOINT (11.30223033 46.10709297)0.8NaN57990.0000000.00.000000
279352013-12-3116:311388503893e61ce711d3Palù del Fersinait16:30POINT (11.35 46.13)T01399251388444400-4.04.20.0NaNNaNPOINT (11.30223033 46.10709297)-0.3NaN60370.0302892.00.015145

Duplicate rows

Most frequently occurring

datetimetimestamp_xusermunicipality.namelanguagehour_blocksgeometrystationelevationtimestamp_yminTemperaturemaxTemperatureprecipitationminWindmaxWindgeometry_ytemperaturewindcellIdtot_curr_cellNR_SITEScurr_site# duplicates
02013-12-3007:091388383749e61ce711d3Sant'Orsola Termeit07:00POINT (11.3 46.11)T01399251388358000-1.85.70.0NaNNaNPOINT (11.30223033 46.10709297)-0.9NaN57990.00.00.02